Truthful ownership transfer with expert advice.

Area of Science:

• Computational Social Choice
• Mechanism Design
• Algorithmic Game Theory

Background:

• Decisions involving external bidders and internal experts, such as corporate mergers or tournament hosting, require balancing bidder values with expert opinions.
• Existing mechanism design solutions like VCG (Vickrey-Clarke-Groves) are not directly applicable to hybrid settings combining monetary transfers (bidders) and no transfers (experts).

Purpose of the Study:

• To design and analyze truthful mechanisms for hybrid social choice settings, maximizing social welfare.
• To address the novel mechanism design problem arising from the combination of transferable and non-transferable utility.

Main Methods:

• Focus on a fundamental scenario with one expert and two bidders.
• Analyze mechanisms utilizing ordinal and cardinal information from bidders and experts.
• Distinguish between mechanisms relying on bidder-only, expert-only, or combined information.

Main Results:

• Provide tight approximation guarantees for optimal social welfare in the considered hybrid setting.
• Demonstrate that the cardinal information setting is rich, enabling non-trivial randomized truthful mechanisms.
• Achieve closer-to-optimal social welfare guarantees in the cardinal setting.

Conclusions:

• The hybrid social choice setting presents unique challenges and opportunities for mechanism design.
• Cardinal information significantly enhances the ability to design effective truthful mechanisms and improve social welfare.
• The study offers a foundational analysis for more complex hybrid social choice problems.